Case management in primary healthcare for people with complex needs to improve integrated care: a large-scale implementation study protocol
Bibliographic record
Abstract
INTRODUCTION: Case management (CM) is among the most studied effective models of integrated care for people with complex needs. The goal of this study is to scale up and assess CM in primary healthcare for people with complex needs. METHODS AND ANALYSIS: The research questions are: (1) which mechanisms contribute to the successful scale-up of CM for people with complex needs in primary healthcare?; (2) how do contextual factors within primary healthcare organisations contribute to these mechanisms? and (3) what are the relationships between the actors, contextual factors, mechanisms and outcomes when scaling-up CM for people with complex needs in primary healthcare? We will conduct a mixed methods Canadian interprovincial project in Quebec, New-Brunswick and Nova Scotia. It will include a scale-up phase and an evaluation phase. At inception, a scale-up committee will be formed in each province to oversee the scale-up phase. We will assess scale-up using a realist evaluation guided by the RAMESES checklist to develop an initial programme theory on CM scale-up. Then we will test and refine the programme theory using a mixed-methods multiple case study with 10 cases, each case being the scalable unit of the intervention in a region. Each primary care clinic within the case will recruit 30 adult patients with complex needs who frequently use healthcare services. Qualitative data will be used to identify contexts, mechanisms and certain outcomes for developing context-mechanism-outcome configurations. Quantitative data will be used to describe patient characteristics and measure scale-up outcomes. ETHICS AND DISSEMINATION: Ethics approval was obtained. Engaging researchers, decision-makers, clinicians and patient partners on the study Steering Committee will foster knowledge mobilisation and impact. The dissemination plan will be developed with the Steering Committee with messages and dissemination methods targeted for each audience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".